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The Challenges of Cyber Resilience in the Maritime Sector: Addressing the Weak Awareness of the Dangers Caused by Cyber Threats
Identification of Ship Maneuvering Behavior Using Singular Value Decomposition-Based Hydrodynamic Variations
Ship Design Optimization for the Effect of Wind Propulsion
Baseline Assessment of Black Sea Food Web Integrity Using a Zooplankton-Based Approach Under the Marine Strategy Framework Directive
Textural, Mineralogical and Chromatic Characterisation of the Beach Sediments of Cuba: Management Implications

Journal Description

Journal of Marine Science and Engineering

Journal of Marine Science and Engineering is an international, peer-reviewed, open access journal on marine science and engineering, published monthly online by MDPI. The Australia New Zealand Marine Biotechnology Society (ANZMBS) is affiliated with JMSE and their members receive discounts on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed with Scopus, SCIE (Web of Science), Ei Compendex, GeoRef, Inspec, AGRIS, and other databases.
Journal Rank: JCR - Q2 (Engineering, Marine) / CiteScore - Q2 (Ocean Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.4 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Journal Clusters of Water Resources: Water, Journal of Marine Science and Engineering, Hydrology, Resources, Oceans, Limnological Review, Coasts.

Impact Factor: 2.8 (2024); 5-Year Impact Factor: 2.8 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2077-1312

Latest Articles

34 pages, 9392 KiB

Open AccessArticle

Temperature-Induced Errors in ITTC Model-Ship Extrapolation

by Sang-seok Han, Saishuai Dai, Momchil Terziev, Daejeong Kim, Tahsin Tezdogan, Doojin Jung and Soonseok Song

J. Mar. Sci. Eng. 2025, 13(7), 1203; https://doi.org/10.3390/jmse13071203 - 20 Jun 2025

This study addresses the question: “Does the towing tank water temperature affect the result of model-ship extrapolation?” While it is well-established that temperature variations affect Reynolds numbers and consequently frictional and viscous resistance, this study examines whether the ITTC 1978 extrapolation method properly [...] Read more.

This study addresses the question: “Does the towing tank water temperature affect the result of model-ship extrapolation?” While it is well-established that temperature variations affect Reynolds numbers and consequently frictional and viscous resistance, this study examines whether the ITTC 1978 extrapolation method properly compensates for these effects. Although current procedures consider temperature indirectly through the Reynolds number, they assume that the form factor depends solely on the Froude number and is insensitive to viscosity changes. Our analysis reveals that the form factor is also temperature-sensitive, indicating a fundamental limitation in the conventional approach. This sensitivity arises from the limitations of the ITTC 1957 friction curve and the method’s neglect of temperature-induced variations in the form factor. To quantify the effect of temperature, model-scale CFD simulations were conducted for two ship models (KCS and KVLCC2) at different water temperatures using the ITTC 1978 procedure with Prohaska’s method. The results show that the ship-scale total resistance coefficient (

C_{T}

) can vary by up to 2.8% depending on the water temperature and friction line selection. This demonstrates that the ITTC 1978 performance prediction method fails to adequately compensate for the temperature-induced changes in resistance, which leads to systematic errors in the extrapolated results. Full article

(This article belongs to the Special Issue CFD Applications in Ship and Offshore Hydrodynamics 2nd Edition)

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15 pages, 4580 KiB

Open AccessArticle

Effects of Nutrients on the Phytoplankton Community Structure in Zhanjiang Bay

by Zhen Zeng, Fajin Chen, Qibin Lao and Qingmei Zhu

J. Mar. Sci. Eng. 2025, 13(7), 1202; https://doi.org/10.3390/jmse13071202 - 20 Jun 2025

With rapid economic and social development, eutrophication in coastal areas is currently one of the most severe environmental problems worldwide. However, our understanding of the response of the phytoplankton community structure to the intensification of coastal eutrophication is still relatively limited. Here, seasonal [...] Read more.

With rapid economic and social development, eutrophication in coastal areas is currently one of the most severe environmental problems worldwide. However, our understanding of the response of the phytoplankton community structure to the intensification of coastal eutrophication is still relatively limited. Here, seasonal phytoplankton, environmental factors, and nutrients were investigated in 2009 and 2019 in Zhanjiang Bay, where eutrophication has intensified in recent years, to analyze the variation in nutrient structure and its impact on the phytoplankton community. The results revealed that the DIP and DSI concentrations in 2019 were higher than those in 2009. However, dissolved inorganic nitrogen (DIN) has decreased substantially over the past several decades, which is due mainly to the decrease in anthropogenic nitrogen emissions, the substantial increase in the intrusion of high-salinity seawater, and the high-phosphorus wastewater discharged from urban industries. This resulted in a decrease in phytoplankton cell abundance, phytoplankton composition, and species diversity (H′) in 2019 compared with 2009. In addition, the superior N transport, storage, and response strategy to a low N supply of diatoms, especially Skeletonema and Chaetoceros, might benefit the growth of diatoms under low DIN conditions. The lower DIN/DIP ratio in 2019 favored the growth of diatoms, especially Skeletonema and Chaetoceros, leading diatoms to dominate the phytoplankton assemblage. This study demonstrates how changes in nutrient structure alter the community structure of phytoplankton, providing new insights into deepening our understanding of eco-environmental evolution. Full article

(This article belongs to the Section Chemical Oceanography)

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17 pages, 1347 KiB

Open AccessOpinion

Early Perspectives on the Planned Brazilian Program to Address Ship-Sourced Pollution

by Daniel Constantino Zacharias and Angelo Teixeira Lemos

J. Mar. Sci. Eng. 2025, 13(7), 1201; https://doi.org/10.3390/jmse13071201 - 20 Jun 2025

A new integrated multi-user monitoring system for Brazilian Jurisdictional Waters (BJW), developed by Instituto Nacional de Pesquisas Espaciais (INPE) with participation from leading universities and research centers in Brazil, proposes a national approach to address oil spills in the South Atlantic. The system [...] Read more.

A new integrated multi-user monitoring system for Brazilian Jurisdictional Waters (BJW), developed by Instituto Nacional de Pesquisas Espaciais (INPE) with participation from leading universities and research centers in Brazil, proposes a national approach to address oil spills in the South Atlantic. The system incorporates a range of technologies, such as satellite data, AI algorithms, autonomous sensors, and high-resolution modeling, to detect and respond to oil spills and maritime threats. This initiative not only aims to strengthen Brazil’s readiness to address the oil spills but also contribute to the protection of BJW resources and ecosystems. This opinion paper presents third-party viewpoints on SisMOM, analyzing both the positive and negative aspects of the project. It also explores some expectations for SisMOM, including some main and alternative methodologies. This article only reflects the authors’ perspectives, interpretations, points of view, opinions, and discussions about SisMOM’s propositions. This paper does NOT represent an official communication of the program, nor its methodologies and developments. Full article

(This article belongs to the Section Marine Pollution)

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19 pages, 4911 KiB

Open AccessArticle

A Novel Trajectory Repairing Model Based on the Artificial Potential Field-Enhanced A* Algorithm for Small Coastal Vessels

by Chengqiang Yu, Zhonglian Jiang, Xinliang Zhang, Wei He and Cheng Zhong

J. Mar. Sci. Eng. 2025, 13(7), 1200; https://doi.org/10.3390/jmse13071200 - 20 Jun 2025

High-completeness ship trajectory data are critical for analyzing navigation behavior characteristics and enhancing effective maritime management. To address the common issues of prolonged AIS data loss for small coastal vessels in nearshore waters, an intelligent trajectory repairing model based on the artificial potential [...] Read more.

High-completeness ship trajectory data are critical for analyzing navigation behavior characteristics and enhancing effective maritime management. To address the common issues of prolonged AIS data loss for small coastal vessels in nearshore waters, an intelligent trajectory repairing model based on the artificial potential field-enhanced A* algorithm (APF-A*) has been proposed. Kernel density estimation was utilized to quantify the distribution characteristics of vessels, thereby constructing an attractive potential field based on historical trajectories and a repulsive potential field based on coastal terrain. Speed distribution characteristics were extracted from historical trajectory points in different regions; on the basis of this, the A* algorithm, integrated with attractive and repulsive fields, was proposed to repair missing trajectory segments. Based on the speed distribution characteristics, time intervals, and distance information, the temporal information of the vessel trajectories was effectively reconstructed. The present study fills the research gap in AIS data reconstruction for small coastal vessels in complex coastal waters. A case study has been conducted in Luoyuan Bay, Fujian Province, China, to further validate the proposed model. The results demonstrate that the trajectory repairing model based on the artificial potential field-enhanced A* algorithm outperformed other models. More specifically, the Hausdorff Distance and Dynamic Time Warping (DTW) metrics decreased by 81.67% and 91.56%, respectively. The present study shares useful insights into intelligent maritime management and further supports accident prevention in coastal waters. Full article

(This article belongs to the Section Ocean Engineering)

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10 pages, 395 KiB

Open AccessArticle

Physicochemical Characterization of Desert Bay with Brine Discharge: A Case Study from Caldera Bay, Northern Chile

by Estefanía Bonnail, Yesenia Rojas-Lillo, T. Ángel DelValls and Edgardo Cruces

J. Mar. Sci. Eng. 2025, 13(7), 1199; https://doi.org/10.3390/jmse13071199 - 20 Jun 2025

Seawater desalination is considered the first option to meet the domestic and industrial requirements of freshwater in desert areas, such as the Atacama Desert (Chile). However, its environmental implications remain poorly characterized. This study evaluated the effects of brine discharge from a desalination [...] Read more.

Seawater desalination is considered the first option to meet the domestic and industrial requirements of freshwater in desert areas, such as the Atacama Desert (Chile). However, its environmental implications remain poorly characterized. This study evaluated the effects of brine discharge from a desalination plant located in Caldera Bay, where fishing and tourism coexist. Sampling was conducted at increasing distances from the outfall to assess physicochemical parameters, sediment metal content, and nutrient concentrations. The results revealed a clear spatial gradient: salinity decreased from 57.75 to 34.87 PSU and nitrate from 10.49 to 4.05 µM. The sediment samples near the outfall showed elevated concentrations of Al, Fe, and Cr(VI). These findings suggest that brine discharge alters water chemistry and sediment quality. This study highlights the need for long-term environmental monitoring and regulatory frameworks to ensure sustainable desalination in sensitive coastal systems. Full article

(This article belongs to the Section Marine Environmental Science)

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21 pages, 2793 KiB

Open AccessArticle

Enhancing Fault Detection in AUV-Integrated Navigation Systems: Analytical Models and Deep Learning Methods

by Huibao Yang, Bangshuai Li, Xiujing Gao, Bo Xiao and Hongwu Huang

J. Mar. Sci. Eng. 2025, 13(7), 1198; https://doi.org/10.3390/jmse13071198 - 20 Jun 2025

In complex underwater environments, the stability of navigation for autonomous underwater vehicles (AUVs) is critical for mission success. To enhance the reliability of the AUV-integrated navigation system, fault detection technology was investigated. Initially, the causes and classifications of faults within the integrated navigation [...] Read more.

In complex underwater environments, the stability of navigation for autonomous underwater vehicles (AUVs) is critical for mission success. To enhance the reliability of the AUV-integrated navigation system, fault detection technology was investigated. Initially, the causes and classifications of faults within the integrated navigation system were analyzed in detail, and these faults were categorized as either abrupt or gradual, based on variations in sensor output characteristics under fault conditions. To overcome the limitations of the residual chi-square method in detecting gradual faults, a cumulative residual detection approach with error coefficient amplification was proposed. The algorithm enhances gradual fault detection by using eigenvalue analysis and constructing fault-frequency-based error amplification coefficients with non-parametric techniques. Furthermore, to improve the detection of gradual faults, artificial intelligence-based fault detection methods were also explored. Specifically, the particle swarm optimization (PSO) algorithm was employed to optimize the hyperparameters of a long short-term memory (LSTM) neural network, leading to the development of a PSO-LSTM fault detection model. In this model, the fault detection function was formulated by comparing the predictions generated by the PSO-LSTM model with those derived from the Kalman filter. The experimental results demonstrated that the fault detection function formulated by PSO-LSTM exhibited enhanced sensitivity to gradual faults and enabled the timely isolation of faulty sensors. In unfamiliar marine regions, the PSO-LSTM method demonstrates greater stability and avoids the need to recalibrate detection thresholds for each sea area—an important advantage for AUV autonomous navigation in complex environments. Full article

(This article belongs to the Section Ocean Engineering)

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22 pages, 12919 KiB

Open AccessArticle

Vibration Control of Deepwater Offshore Platform Using Viscous Dampers Under Wind, Wave, and Earthquake

by Kaien Jiang, Huiyang Li, Guoer Lv, Lizhong Wang, Lilin Wang and Huafeng Yu

J. Mar. Sci. Eng. 2025, 13(7), 1197; https://doi.org/10.3390/jmse13071197 - 20 Jun 2025

This study investigates the use of viscous dampers (VDs) to reduce the vibration of a deepwater offshore platform under joint wind, wave, and earthquake action. A finite element model was established based on the Opensees software (version 3.7.1), incorporating soil–structure interaction simulated by [...] Read more.

This study investigates the use of viscous dampers (VDs) to reduce the vibration of a deepwater offshore platform under joint wind, wave, and earthquake action. A finite element model was established based on the Opensees software (version 3.7.1), incorporating soil–structure interaction simulated by the nonlinear Winkler springs and simulating hydrodynamic loads via the Morison equation. Turbulent wind fields were generated using the von Kármán spectrum, and irregular wave profiles were synthesized from the JONSWAP spectrum. The 1995 Kobe earthquake record served as seismic input. The time-history dynamic response for the deepwater offshore platform was evaluated under two critical scenarios: isolated seismic excitation and the joint action of wind, wave, and seismic loading. The results demonstrate that VDs configured diagonally at each structural level effectively suppress platform vibrations under both isolated seismic and wind–wave–earthquake conditions. Under seismic excitation, the VD system reduced maximum deck acceleration, velocity, displacement, and base shear force by 9.95%, 22.33%, 14%, and 31.08%, respectively. For combined environmental loads, the configuration achieved 15.87%, 21.48%, 13.51%, and 34.31% reductions in peak deck acceleration, velocity, displacement, and base shear force, respectively. Moreover, VD parameter analysis confirms that increased damping coefficients enhance control effectiveness. Full article

(This article belongs to the Section Ocean Engineering)

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21 pages, 8446 KiB

Open AccessArticle

Regional Wave Analysis in the East China Sea Based on the SWAN Model

by Songnan Ma, Fuwu Ji, Qunhui Yang, Zhinan Mi and Wenhui Cao

J. Mar. Sci. Eng. 2025, 13(6), 1196; https://doi.org/10.3390/jmse13061196 - 19 Jun 2025

High-precision wave data serve as a foundation for investigating the wave characteristics of the East China Sea (ECS) and wave energy development. Based on the simulating waves nearshore (SWAN) model, this study uses the ERA5 (ECMWF Reanalysis v5) reanalysis wind field data and [...] Read more.

High-precision wave data serve as a foundation for investigating the wave characteristics of the East China Sea (ECS) and wave energy development. Based on the simulating waves nearshore (SWAN) model, this study uses the ERA5 (ECMWF Reanalysis v5) reanalysis wind field data and ETOPO1 bathymetric data to perform high-precision simulations at a resolution of 0.05° × 0.05° for the waves in the area of 25–35° N and 120–130° E in the ECS from 2009 to 2023. The simulation results indicate that the application of the whitecapping dissipation parameter Komen and the bottom friction parameter Collins yields an average RMSE of 0.374 m and 0.369 m when compared to satellite-measured data, demonstrating its superior suitability for wave simulation in shallow waters such as the ESC over the other whitecapping dissipation parameter, Westhuysen, and the other two bottom friction parameters, Jonswap and Madsen, in the SWAN model. The monthly average significant wave height (SWH) ranges from 0 to 3 m, exhibiting a trend that it is more important in autumn and winter than in spring and summer and gradually increases from the northwest to the southeast. Due to the influence of the Kuroshio current, topography, and events such as typhoons, areas with significant wave heights are found in the northwest of the Ryukyu Islands and north of the Taiwan Strait. The wave energy flux density in most areas of the ECS is >2 kW/m, particularly in the north of the Ryukyu Islands, where the annual average value remains above 8 kW/m. Because of the influence of climate events such as El Niño and extreme heatwaves, the wave energy flux density decreased significantly in some years (a 21% decrease in 2015). The coefficient of variation of wave energy in the East China Sea exhibits pronounced regional heterogeneity, which can be categorized into four distinct patterns: high mean wave energy with high variation coefficient, high mean wave energy with low variation coefficient, low mean wave energy with high variation coefficient, and low mean wave energy with low variation coefficient. This classification fundamentally reflects the intrinsic differences in dynamic environments across various maritime regions. These high-precision numerical simulation results provide methodological and theoretical support for exploring the spatiotemporal variation laws of waves in the ECS region, the development and utilization of wave resources, and marine engineering construction. Full article

(This article belongs to the Section Physical Oceanography)

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24 pages, 3569 KiB

Open AccessArticle

Semi-Supervised Underwater Image Enhancement Method Using Multimodal Features and Dynamic Quality Repository

by Mu Ding, Gen Li, Yu Hu, Hangfei Liu, Qingsong Hu and Xiaohua Huang

J. Mar. Sci. Eng. 2025, 13(6), 1195; https://doi.org/10.3390/jmse13061195 - 19 Jun 2025

Obtaining clear underwater images is crucial for smart aquaculture, so it is necessary to repair degraded underwater images. Although underwater image restoration techniques have achieved remarkable results in recent years, the scarcity of labeled data poses a significant challenge to continued advancement. It [...] Read more.

Obtaining clear underwater images is crucial for smart aquaculture, so it is necessary to repair degraded underwater images. Although underwater image restoration techniques have achieved remarkable results in recent years, the scarcity of labeled data poses a significant challenge to continued advancement. It is well known that semi-supervised learning can make use of unlabeled data. In this study, we proposed a semi-supervised underwater image enhancement method, MCR-UIE, which utilized multimodal contrastive learning and a dynamic quality reliability repository to leverage the unlabeled data during training. This approach used multimodal feature contrast regularization to prevent the overfitting of incorrect labels, and secondly, introduced a dynamic quality reliability repository to update the output as pseudo ground truth. The robustness and generalization of the model in pseudo-label generation and unlabeled data learning were improved. Extensive experiments conducted on the UIEB and LSUI datasets demonstrated that the proposed method consistently outperformed existing traditional and deep learning-based approaches in both quantitative and qualitative evaluations. Furthermore, its successful application to images captured from deep-sea cage aquaculture environments validated its practical value. These results indicated that MCR-UIE held strong potential for real-world deployment in intelligent monitoring and visual perception tasks in complex underwater scenarios. Full article

(This article belongs to the Special Issue Selection of Deep-Sea Aquaculture Species and Development of Supporting Technologies and Equipment)

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25 pages, 4997 KiB

Open AccessArticle

Use of Machine-Learning Techniques to Estimate Long-Term Wave Power at a Target Site Where Short-Term Data Are Available

by María José Pérez-Molina and José A. Carta

J. Mar. Sci. Eng. 2025, 13(6), 1194; https://doi.org/10.3390/jmse13061194 - 19 Jun 2025

Wave energy is a promising renewable resource supporting the decarbonization of energy systems. However, its significant temporal variability necessitates long-term datasets for accurate resource assessment. A common approach to obtaining such data is through climate reanalysis datasets. Nevertheless, reanalysis data may not accurately [...] Read more.

Wave energy is a promising renewable resource supporting the decarbonization of energy systems. However, its significant temporal variability necessitates long-term datasets for accurate resource assessment. A common approach to obtaining such data is through climate reanalysis datasets. Nevertheless, reanalysis data may not accurately capture the local characteristics of wave energy at specific sites. This study proposes a supervised machine-learning (ML) approach to estimate long-term wave energy at locations with only short-term in situ measurements. The method involves training ML models using concurrent short-term buoy data and ERA5 reanalysis data, enabling the extension of wave energy estimates over longer periods using only reanalysis inputs. As a case study, hourly mean significant wave height and energy period data from 2000 to 2023 were analyzed, collected by a deep-water buoy off the coast of Gran Canaria (Canary Islands, Spain). Among the ML techniques evaluated, Multiple Linear Regression (MLR) and Support Vector Regression yielded the most favorable error metrics. MLR was selected due to its lower computational complexity, greater interpretability, and ease of implementation, aligning with the principle of parsimony, particularly in contexts where model transparency is essential. The MLR model achieved a mean absolute error (MAE) of 2.56 kW/m and a root mean square error (RMSE) of 4.49 kW/m, significantly outperforming the direct use of ERA5 data, which resulted in an MAE of 4.38 kW/m and an RMSE of 7.1 kW/m. These findings underscore the effectiveness of the proposed approach in enhancing long-term wave energy estimations using limited in situ data. Full article

(This article belongs to the Special Issue Development and Utilization of Offshore Renewable Energy)

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20 pages, 10753 KiB

Open AccessArticle

Physics-Guided Self-Supervised Learning Full Waveform Inversion with Pretraining on Simultaneous Source

by Qiqi Zheng, Meng Li and Bangyu Wu

J. Mar. Sci. Eng. 2025, 13(6), 1193; https://doi.org/10.3390/jmse13061193 - 19 Jun 2025

Full waveform inversion (FWI) is an established precise velocity estimation tool for seismic exploration. Machine learning-based FWI could plausibly circumvent the long-standing cycle-skipping problem of traditional model-driven methods. The physics-guided self-supervised FWI is appealing in that it avoids having to make tedious efforts [...] Read more.

Full waveform inversion (FWI) is an established precise velocity estimation tool for seismic exploration. Machine learning-based FWI could plausibly circumvent the long-standing cycle-skipping problem of traditional model-driven methods. The physics-guided self-supervised FWI is appealing in that it avoids having to make tedious efforts in terms of label generation for supervised methods. One way is to employ an inversion network to convert the seismic shot gathers into a velocity model. The objective function is to minimize the difference between the recorded seismic data and the synthetic data by solving the wave equation using the inverted velocity model. To further improve the efficiency, we propose a two-stage training strategy for the self-supervised learning FWI. The first stage is to pretrain the inversion network using a simultaneous source for a large-scale velocity model with high efficiency. The second stage is switched to modeling the separate shot gathers for an accurate measurement of the seismic data to invert the velocity model details. The inversion network is a partial convolution attention modified UNet (PCAMUNet), which combines local feature extraction with global information integration to achieve high-resolution velocity model estimation from seismic shot gathers. The time-domain 2D acoustic wave equation serves as the physical constraint in this self-supervised framework. Different loss functions are used for the two stages, that is, the waveform loss with time weighting for the first stage (simultaneous source) and the hybrid waveform with time weighting and logarithmic envelope loss for the second stage (separate source). Comparative experiments demonstrate that the proposed approach improves both inversion accuracy and efficiency on the Marmousi2 model, Overthrust model, and BP model tests. Moreover, the method exhibits excellent noise resistance and stability when low-frequency data component is missing. Full article

(This article belongs to the Special Issue Modeling and Waveform Inversion of Marine Seismic Data)

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22 pages, 3923 KiB

Open AccessArticle

Optimizing Fuel Efficiency and Emissions of Marine Diesel Engines When Using Biodiesel Mixtures Under Diverse Load/Temperature Conditions: Predictive Model and Comprehensive Life Cycle Analysis

by Kwang-Sik Jo, Kyeong-Ju Kong and Seung-Hun Han

J. Mar. Sci. Eng. 2025, 13(6), 1192; https://doi.org/10.3390/jmse13061192 - 19 Jun 2025

Marine transportation contributes approximately 2.5% of global greenhouse gas emissions. While previous studies have examined biodiesel effects on automotive engines, research on marine applications reveals critical gaps: (1) existing studies focus on single-parameter analysis without considering the complex interactions between biodiesel ratio, engine [...] Read more.

Marine transportation contributes approximately 2.5% of global greenhouse gas emissions. While previous studies have examined biodiesel effects on automotive engines, research on marine applications reveals critical gaps: (1) existing studies focus on single-parameter analysis without considering the complex interactions between biodiesel ratio, engine load, and operating conditions; (2) most research lacks comprehensive lifecycle assessment integration with real-time operational data; (3) previous optimization models demonstrate insufficient accuracy (R² < 0.80) for practical marine applications; and (4) no adaptive algorithms exist for dynamic biodiesel ratio adjustment based on operational conditions. These limitations prevent effective biodiesel implementation in maritime operations, necessitating an integrated multi-parameter optimization approach. This study addresses this research gap by proposing an integrated optimization model for fuel efficiency and emissions of marine diesel engines using biodiesel mixtures under diverse operating conditions. Based on extensive experimental data from two representative marine engines (YANMAR 6HAL2-DTN 200 kW and Niigatta Engineering 6L34HX 2471 kW), this research analyzes correlations between biodiesel blend ratios (pure diesel, 20%, 50%, and 100% biodiesel), engine load conditions (10–100%), and operating temperature with nitrogen oxides, carbon dioxide, and carbon monoxide emissions. Multivariate regression models were developed, allowing prediction of emission levels with high accuracy (R² = 0.89–0.94). The models incorporated multiple parameters, including engine characteristics, fuel properties, and ambient conditions, to provide a comprehensive analytical framework. Life cycle assessment (LCA) results show that the B50 biodiesel ratio achieves optimal environmental efficiency, reducing greenhouse gases by 15% compared to B0 while maintaining stable engine performance across operational profiles. An adaptive optimization algorithm for operating conditions is proposed, providing detailed reference charts for ship operators on ideal biodiesel ratios based on load conditions, ambient temperature, and operational priorities in different maritime zones. The findings demonstrate significant potential for emissions reduction in the maritime sector through strategic biodiesel implementation. Full article

(This article belongs to the Section Ocean Engineering)

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11 pages, 335 KiB

Open AccessArticle

Consistent Models of Flexural-Gravity Waves in Floating Ice

by Alexander Korobkin and Tatiana Khabakhpasheva

J. Mar. Sci. Eng. 2025, 13(6), 1191; https://doi.org/10.3390/jmse13061191 - 19 Jun 2025

Classification of flexural-gravity waves in floating ice is discussed, using the dispersion relation of these waves and the criterion of sea ice breaking, based on the concept of yield strain. It is shown that flexural-gravity waves, in terms of their wavelength and amplitude, [...] Read more.

Classification of flexural-gravity waves in floating ice is discussed, using the dispersion relation of these waves and the criterion of sea ice breaking, based on the concept of yield strain. It is shown that flexural-gravity waves, in terms of their wavelength and amplitude, are divided into waves with both inertia and ice rigidity being of major importance, waves with negligible ice inertia, waves with negligible ice rigidity (broken ice) and gravity waves. The effects of water depth and ice thickness on the waves are also investigated. Full article

(This article belongs to the Special Issue Hydroelastic Behaviour of Floating Offshore Structures)

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18 pages, 5615 KiB

Open AccessArticle

Experimental Investigation on IceBreaking Resistance and Ice Load Distribution for Comparison of Icebreaker Bows

by Xuhao Gang, Yukui Tian, Chaoge Yu, Ying Kou and Weihang Zhao

J. Mar. Sci. Eng. 2025, 13(6), 1190; https://doi.org/10.3390/jmse13061190 - 18 Jun 2025

During icebreaker navigation in ice-covered waters, icebreaking resistance and dynamic ice loads acting on the bow critically determine the vessel’s icebreaking performance. Quantitative characterization of the icebreaking resistance behavior and ice load distribution on the bow is essential for elucidating ship-ice interaction mechanisms, [...] Read more.

During icebreaker navigation in ice-covered waters, icebreaking resistance and dynamic ice loads acting on the bow critically determine the vessel’s icebreaking performance. Quantitative characterization of the icebreaking resistance behavior and ice load distribution on the bow is essential for elucidating ship-ice interaction mechanisms, assessing icebreaking capability, and optimizing structural design. This study conducted comparative icebreaking tests on two icebreaker bow models with distinct geometries in the small ice model basin of China Ship Scientific Research Center (CSSRC SIMB). Systematic measurements were performed to quantify icebreaking resistance, capture spatiotemporal ice load distributions, and document ice failure patterns under level ice conditions. The analysis reveals that bow geometry profoundly influences icebreaking efficiency: the stem angle governs the proportion of bending failure during vertical ice penetration, while the flare angle modulates circumferential failure modes along the hull-ice interface. Notably, the sunken keel configuration enhances ice clearance by mechanically expelling fractured ice blocks. Ice load distributions exhibit pronounced nonlinearity, with localized pressure concentrations and stochastic load center migration driven by ice fracture dynamics. Furthermore, icebreaking patterns—such as fractured ice dimensions and kinematic behavior during ship-ice interaction—are quantitatively correlated with the bow designs. These experimentally validated findings provide critical insights into ice-structure interaction physics, offering an empirical foundation for performance prediction and bow-form optimization in the preliminary design of icebreakers. Full article

(This article belongs to the Special Issue Navigation Performance and Experimental Research of Ships in Ice-Covered Areas)

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17 pages, 4356 KiB

Open AccessArticle

Impact of High-Concentration Biofuels on Cylinder Lubricating Oil Performance in Low-Speed Two-Stroke Marine Diesel Engines

by Enrui Zhao, Guichen Zhang, Qiuyu Li and Saihao Zhu

J. Mar. Sci. Eng. 2025, 13(6), 1189; https://doi.org/10.3390/jmse13061189 - 18 Jun 2025

With the implementation of the ISO 8217-2024 marine fuel standard, the use of high-concentration biofuels in ships has become viable. However, relatively few studies have been conducted on the effects of biofuels on cylinder lubrication performance in low-speed, two-stroke marine diesel engines. In [...] Read more.

With the implementation of the ISO 8217-2024 marine fuel standard, the use of high-concentration biofuels in ships has become viable. However, relatively few studies have been conducted on the effects of biofuels on cylinder lubrication performance in low-speed, two-stroke marine diesel engines. In this study, catering waste oil was blended with 180 cSt low-sulfur fuel oil (LSFO) to prepare biofuels with volume fractions of 24% (B24) and 50% (B50). These biofuels were evaluated in a MAN marine diesel engine under load conditions of 25%, 50%, 75%, and 90%. The experimental results showed that, at the same engine load, the use of B50 biofuel led to lower kinematic viscosity and oxidation degree of the cylinder residual oil, but higher total base number (TBN), nitration level, PQ index, and concentrations of wear elements (Fe, Cu, Cr, Mo). These results indicate that the wear of the cylinder liner–piston ring interface was more severe when using B50 biofuel than when using B24 biofuel. For the same type of fuel, as the engine load increased, the kinematic viscosity and TBN of the residual oil decreased, while the PQ index and the concentrations of Fe, Cu, Cr, and Mo increased, reflecting the aggravated wear severity. Ferrographic analysis further revealed that ferromagnetic wear particles in the oil mainly consisted of normal wear debris. When using B50 biodiesel, a small amount of fatigue wear particles were detected. These findings offer crucial insights for optimizing biofuel utilization and improving cylinder lubrication systems in marine engines. Full article

(This article belongs to the Section Ocean Engineering)

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20 pages, 1743 KiB

Open AccessArticle

Understanding Wave Attenuation Across Marshes: Insights from Numerical Modeling

by Madeline R. Foster-Martinez, Ioannis Y. Georgiou, Duncan M. FitzGerald, Zoe J. Hughes, Alyssa Novak and Md Mohiuddin Sakib

J. Mar. Sci. Eng. 2025, 13(6), 1188; https://doi.org/10.3390/jmse13061188 - 18 Jun 2025

Marsh vegetation dampens wave energy, providing protection to coastal communities from storms. A new modeling framework was applied to study wave height evolution over the saltmarsh bordering Newbury, MA. A regional Delft3D hydrodynamic model generated wind driver waves in the open water portions [...] Read more.

Marsh vegetation dampens wave energy, providing protection to coastal communities from storms. A new modeling framework was applied to study wave height evolution over the saltmarsh bordering Newbury, MA. A regional Delft3D hydrodynamic model generated wind driver waves in the open water portions of the study area, which were then one-way coupled with an analytical model, the Marsh Transect Wave Attenuation (MTWA) model, which tracked wave evolution along select transects throughout the marsh. Field observations of vegetation and wave height evolution were used to calibrate MTWA. Seven scenarios were run covering a range of possible future management and environmental conditions, in addition to projected sea level rise. Results underscore the importance of vegetation and elevation to wave attenuation. Full article

(This article belongs to the Special Issue Learning from Geomorphological Adaptation of Coasts at Different Time Scales (2nd Edition))

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16 pages, 3012 KiB

Open AccessReview

Application of Large-Scale Rotating Platforms in the Study of Complex Oceanic Dynamic Processes

by Xiaojie Lu, Guoqing Han, Yifan Lin, Qian Cao, Zhiwei You, Jingyuan Xue, Xinyuan Zhang and Changming Dong

J. Mar. Sci. Eng. 2025, 13(6), 1187; https://doi.org/10.3390/jmse13061187 - 18 Jun 2025

As the core components of geophysical dynamic system, oceans and atmospheres are dominated by the Coriolis force, which governs complex dynamic phenomena such as internal waves, gravity currents, vortices, and others involving multi-scale spatiotemporal coupling. Due to the limitations of in situ observations, [...] Read more.

As the core components of geophysical dynamic system, oceans and atmospheres are dominated by the Coriolis force, which governs complex dynamic phenomena such as internal waves, gravity currents, vortices, and others involving multi-scale spatiotemporal coupling. Due to the limitations of in situ observations, large-scale rotating tanks have emerged as critical experimental platforms for simulating Earth’s rotational effects. This review summarizes recent advancements in rotating tank applications for studying oceanic flow phenomena, including mesoscale eddies, internal waves, Ekman flows, Rossby waves, gravity currents, and bottom boundary layer dynamics. Advanced measurement techniques, such as particle image velocimetry (PIV) and planar laser-induced fluorescence (PLIF), have enabled quantitative analyses of internal wave breaking-induced mixing and refined investigations of vortex merging dynamics. The findings demonstrate that large-scale rotating tanks provide a controllable experimental framework for unraveling the physical essence of geophysical fluid motions. Such laboratory experimental endeavors in a rotating tank can be applied to more extensive scientific topics, in which the rotation and stratification play important roles, offering crucial support for climate model parameterization and coupled ocean–land–atmosphere mechanisms. Full article

(This article belongs to the Section Physical Oceanography)

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32 pages, 5632 KiB

Open AccessArticle

One-Dimensional Plume Dispersion Modeling in Marine Conditions (SEDPLUME1D-Model)

by L. C. van Rijn

J. Mar. Sci. Eng. 2025, 13(6), 1186; https://doi.org/10.3390/jmse13061186 - 18 Jun 2025

Dredging of fine sediments and dumping of fines at disposal sites produce passive plumes behind the dredging equipment. Each type of dredging method has its own plume characteristics. All types of dredging operations create some form of turbidity (spillage of dredged materials) in [...] Read more.

Dredging of fine sediments and dumping of fines at disposal sites produce passive plumes behind the dredging equipment. Each type of dredging method has its own plume characteristics. All types of dredging operations create some form of turbidity (spillage of dredged materials) in the water column, depending on (i) the applied method (mechanical grab/backhoe, hydraulic suction dredging with/without overflow), (ii) the nature of the sediment bed, and (iii) the hydrodynamic conditions. A simple parameter to represent the spillage of dredged materials is the spill percentage (R_spill) of the initial load. In the case of cutter dredging and hopper dredging without overflow, sediment spillage is mostly low, with values in the range of 1% to 3%, The spill percentage is higher, in the range of 3% to 30%, for hopper dredging of mud with intensive overflow. Spilling of dredged materials also occurs at disposal sites. The spill percentage is generally low, with values in the range of 1% to 3%, if the load is dumped through bottom doors in deep water, creating a dynamic plume which descends rapidly to the bottom with cloud velocities of 1 m/s. The most accurate approach to study passive plume behavior is the application of a 3D model, which, however, is a major, time-consuming effort. A practical 1D plume dispersion model can help to identify the best parameter settings involved and to conduct fast scan studies. The proposed 1D model represents equations for dynamic plume behavior, as well as passive plume behavior including advection, diffusion and settling processes. Full article

(This article belongs to the Section Marine Environmental Science)

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28 pages, 7802 KiB

Open AccessArticle

Anomalous Behavior in Weather Forecast Uncertainty: Implications for Ship Weather Routing

by Marijana Marjanović, Jasna Prpić-Oršić, Anton Turk and Marko Valčić

J. Mar. Sci. Eng. 2025, 13(6), 1185; https://doi.org/10.3390/jmse13061185 - 17 Jun 2025

Ship weather routing is heavily dependent on weather forecasts. However, the predictive nature of meteorological models introduces an unavoidable level of uncertainty which, if not accounted for, can compromise navigational safety, operational efficiency, and environmental impact. This study examines the temporal degradation of [...] Read more.

Ship weather routing is heavily dependent on weather forecasts. However, the predictive nature of meteorological models introduces an unavoidable level of uncertainty which, if not accounted for, can compromise navigational safety, operational efficiency, and environmental impact. This study examines the temporal degradation of forecast accuracy across certain oceanographic and atmospheric variables, using a six-month dataset for the area of North Atlantic provided by the National Oceanic and Atmospheric Administration (NOAA). The analysis reveals distinct variable-specific uncertainty trends with wind speed forecasts exhibiting significant temporal fluctuation (RMSE increasing from 0.5 to 4.0 m/s), while significant wave height forecasts degrade in a more stable and predictable pattern (from 0.2 to 0.9 m). Confidence intervals also exhibit non-monotonic evolution, narrowing by up to 15% between 96–120-h lead times. To address these dynamics, a Python-based framework combines distribution-based modeling with calibrated confidence intervals to generate uncertainty bounds that evolve with forecast lead time (R² = 0.87–0.93). This allows uncertainty to be quantified not as a static estimate, but as a function sensitive to both variable type and prediction horizon. When integrated into routing algorithms, such representations allow for route planning strategies that are not only more reflective of real-world meteorological limitations but also more robust to evolving weather conditions, demonstrated by a 3–7% increase in travel time in exchange for improved safety margins across eight test cases. Full article

(This article belongs to the Section Ocean Engineering)

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29 pages, 4445 KiB

Open AccessArticle

Link Availability-Aware Routing Metric Design for Maritime Mobile Ad Hoc Network

by Shuaiheng Huai, Tianrui Liu, Yi Jiang, Yanpeng Dai, Feng Xue and Qing Hu

J. Mar. Sci. Eng. 2025, 13(6), 1184; https://doi.org/10.3390/jmse13061184 - 17 Jun 2025

A maritime mobile ad hoc network (M-MANET) is an essential part of the maritime communication network and plays a key role in many maritime scenarios. However, the topology of M-MANET dynamically changes with the movement of vessels, which leads to unstable link states [...] Read more.

A maritime mobile ad hoc network (M-MANET) is an essential part of the maritime communication network and plays a key role in many maritime scenarios. However, the topology of M-MANET dynamically changes with the movement of vessels, which leads to unstable link states and poses the risk of data transmission interruption. In this paper, a mobility model for small unmanned surface vessels based on smooth Gaussian semi-Markovian and a trajectory prediction method for large vessels based on a bi-directional long short-term memory network are proposed to better simulate the nodes’ movement in the M-MANET. Then, a link available based routing metric is proposed for M-MANET scenarios, which incorporates factors of mobility model and vessel trajectory. Experiments demonstrate that compared with the benchmark methods, the proposed mobility model depicts the movement characteristics of vessels more accurately, the proposed trajectory prediction method achieves higher prediction accuracy and stability, the proposed routing metric scheme has a reduction of 14.59% in end-to-end delay, a 1.54% increase in packet delivery fraction, and a 4.43% increase in network throughput on average. Full article

(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)

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